Overview

Dataset statistics

Number of variables26
Number of observations2401273
Missing cells58
Missing cells (%)< 0.1%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory476.3 MiB
Average record size in memory208.0 B

Variable types

CAT12
NUM12
BOOL2

Reproduction

Analysis started2021-09-14 01:08:08.053294
Analysis finished2021-09-14 01:13:26.483894
Duration5 minutes and 18.43 seconds
Versionpandas-profiling v2.8.0
Command linepandas_profiling --config_file config.yaml [YOUR_FILE.csv]
Download configurationconfig.yaml

Warnings

GRID_TYPE has constant value "Shot Chart Detail" Constant
SHOT_ATTEMPTED_FLAG has constant value "1" Constant
PLAYER_NAME has a high cardinality: 1437 distinct values High cardinality
ACTION_TYPE has a high cardinality: 65 distinct values High cardinality
PERIOD is highly correlated with GAME_EVENT_IDHigh correlation
GAME_EVENT_ID is highly correlated with PERIODHigh correlation
GAME_DATE is highly correlated with GAME_IDHigh correlation
GAME_ID is highly correlated with GAME_DATEHigh correlation
SHOT_ZONE_BASIC is highly correlated with SHOT_TYPEHigh correlation
SHOT_TYPE is highly correlated with SHOT_ZONE_BASIC and 1 other fieldsHigh correlation
SHOT_ZONE_RANGE is highly correlated with SHOT_TYPEHigh correlation
MINUTES_REMAINING has 244457 (10.2%) zeros Zeros
SECONDS_REMAINING has 66705 (2.8%) zeros Zeros
SHOT_DISTANCE has 256926 (10.7%) zeros Zeros
LOC_X has 153237 (6.4%) zeros Zeros
LOC_Y has 109140 (4.5%) zeros Zeros

Variables

df_index
Real number (ℝ≥0)

Distinct count219458
Unique (%)9.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean100563.7943903088
Minimum0
Maximum219457
Zeros12
Zeros (%)< 0.1%
Memory size18.3 MiB
2021-09-13T22:13:26.690458image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile10005
Q150026
median100053
Q3150079
95-th percentile193433
Maximum219457
Range219457
Interquartile range (IQR)100053

Descriptive statistics

Standard deviation58581.99766
Coefficient of variation (CV)0.5825356732
Kurtosis-1.154057161
Mean100563.7944
Median Absolute Deviation (MAD)50027
Skewness0.04093024395
Sum2.414811242e+11
Variance3431850450
2021-09-13T22:13:26.801169image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
204712< 0.1%
 
12164212< 0.1%
 
11754412< 0.1%
 
11139912< 0.1%
 
11344612< 0.1%
 
10730112< 0.1%
 
10934812< 0.1%
 
10320312< 0.1%
 
10525012< 0.1%
 
9910512< 0.1%
 
Other values (219448)2401153> 99.9%
 
ValueCountFrequency (%) 
012< 0.1%
 
112< 0.1%
 
212< 0.1%
 
312< 0.1%
 
412< 0.1%
 
ValueCountFrequency (%) 
2194571< 0.1%
 
2194561< 0.1%
 
2194551< 0.1%
 
2194541< 0.1%
 
2194531< 0.1%
 

GRID_TYPE
Categorical

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
Shot Chart Detail
2401273
ValueCountFrequency (%) 
Shot Chart Detail2401273100.0%
 
2021-09-13T22:13:26.957757image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length17
Min length17

GAME_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count14198
Unique (%)0.6%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean21456085.03299375
Minimum20900001
Maximum22001080
Zeros0
Zeros (%)0.0%
Memory size18.3 MiB
2021-09-13T22:13:27.101234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20900001
5-th percentile20900736
Q121200235
median21500163
Q321701221
95-th percentile22000401
Maximum22001080
Range1101079
Interquartile range (IQR)500986

Descriptive statistics

Standard deviation341374.9769
Coefficient of variation (CV)0.0159104038
Kurtosis-1.180033895
Mean21456085.03
Median Absolute Deviation (MAD)299876
Skewness-0.04883585296
Sum5.152191768e+13
Variance1.165368749e+11
2021-09-13T22:13:27.213329image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
21600711247< 0.1%
 
21500391244< 0.1%
 
21800928242< 0.1%
 
21401022236< 0.1%
 
21801145234< 0.1%
 
21401184234< 0.1%
 
21800853233< 0.1%
 
21100722233< 0.1%
 
21800920230< 0.1%
 
21800480226< 0.1%
 
Other values (14188)239891499.9%
 
ValueCountFrequency (%) 
20900001142< 0.1%
 
20900002160< 0.1%
 
20900003158< 0.1%
 
20900004172< 0.1%
 
20900005160< 0.1%
 
ValueCountFrequency (%) 
22001080185< 0.1%
 
22001079192< 0.1%
 
22001078202< 0.1%
 
22001077176< 0.1%
 
22001076185< 0.1%
 

GAME_EVENT_ID
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count900
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean276.1711517182761
Minimum1
Maximum1012
Zeros0
Zeros (%)0.0%
Memory size18.3 MiB
2021-09-13T22:13:27.372344image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile22
Q1126
median270
Q3413
95-th percentile570
Maximum1012
Range1011
Interquartile range (IQR)287

Descriptive statistics

Standard deviation174.292407
Coefficient of variation (CV)0.631102872
Kurtosis-0.8910411342
Mean276.1711517
Median Absolute Deviation (MAD)144
Skewness0.2392967739
Sum663162330
Variance30377.84314
2021-09-13T22:13:27.471451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
780200.3%
 
972390.3%
 
271180.3%
 
1165610.3%
 
1362490.3%
 
1560570.3%
 
1760040.3%
 
1659620.2%
 
1959560.2%
 
1459540.2%
 
Other values (890)233615397.3%
 
ValueCountFrequency (%) 
18< 0.1%
 
271180.3%
 
340810.2%
 
451320.2%
 
546080.2%
 
ValueCountFrequency (%) 
10121< 0.1%
 
9861< 0.1%
 
9841< 0.1%
 
9821< 0.1%
 
9801< 0.1%
 

PLAYER_ID
Real number (ℝ≥0)

Distinct count1441
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean385108.771863091
Minimum255
Maximum1630466
Zeros0
Zeros (%)0.0%
Memory size18.3 MiB
2021-09-13T22:13:27.603724image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum255
5-th percentile1938
Q1101181
median201967
Q3203487
95-th percentile1628470
Maximum1630466
Range1630211
Interquartile range (IQR)102306

Descriptive statistics

Standard deviation547242.1731
Coefficient of variation (CV)1.421006773
Kurtosis1.306065482
Mean385108.7719
Median Absolute Deviation (MAD)1525
Skewness1.773765552
Sum9.247512959e+11
Variance2.99473996e+11
2021-09-13T22:13:27.733378image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
201566166760.7%
 
2544157270.7%
 
201935146570.6%
 
201942138130.6%
 
201142137060.6%
 
200746135320.6%
 
2546135120.6%
 
201939132930.6%
 
203081125440.5%
 
202689115270.5%
 
Other values (1431)226228694.2%
 
ValueCountFrequency (%) 
25520770.1%
 
28330< 0.1%
 
406664< 0.1%
 
436574< 0.1%
 
46719490.1%
 
ValueCountFrequency (%) 
163046667< 0.1%
 
163027384< 0.1%
 
1630271108< 0.1%
 
16302687< 0.1%
 
1630267315< 0.1%
 

PLAYER_NAME
Categorical

HIGH CARDINALITY

Distinct count1437
Unique (%)0.1%
Missing58
Missing (%)< 0.1%
Memory size18.3 MiB
Russell Westbrook
 
16676
LeBron James
 
15727
James Harden
 
14657
DeMar DeRozan
 
13813
Kevin Durant
 
13706
Other values (1432)
2326636
ValueCountFrequency (%) 
Russell Westbrook166760.7%
 
LeBron James157270.7%
 
James Harden146570.6%
 
DeMar DeRozan138130.6%
 
Kevin Durant137060.6%
 
LaMarcus Aldridge135320.6%
 
Carmelo Anthony135120.6%
 
Stephen Curry132930.6%
 
Damian Lillard125440.5%
 
Kemba Walker115270.5%
 
Other values (1427)226222894.2%
 
2021-09-13T22:13:27.913909image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length24
Median length13
Mean length12.84705154
Min length3

TEAM_ID
Real number (ℝ≥0)

Distinct count30
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean1610612751.4979606
Minimum1610612737
Maximum1610612766
Zeros0
Zeros (%)0.0%
Memory size18.3 MiB
2021-09-13T22:13:28.046596image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1610612737
5-th percentile1610612738
Q11610612744
median1610612752
Q31610612759
95-th percentile1610612765
Maximum1610612766
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.643051807
Coefficient of variation (CV)5.366312789e-09
Kurtosis-1.201686809
Mean1610612751
Median Absolute Deviation (MAD)7
Skewness-0.0003723065345
Sum3.867520914e+15
Variance74.70234453
2021-09-13T22:13:28.157300image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
1610612743814593.4%
 
1610612757813203.4%
 
1610612744812773.4%
 
1610612756812373.4%
 
1610612758811253.4%
 
1610612764811123.4%
 
1610612747808003.4%
 
1610612749807863.4%
 
1610612755806823.4%
 
1610612750806083.4%
 
Other values (20)159086766.3%
 
ValueCountFrequency (%) 
1610612737793253.3%
 
1610612738796833.3%
 
1610612739788533.3%
 
1610612740804783.4%
 
1610612741798843.3%
 
ValueCountFrequency (%) 
1610612766785673.3%
 
1610612765798253.3%
 
1610612764811123.4%
 
1610612763799253.3%
 
1610612762780083.2%
 

TEAM_NAME
Categorical

Distinct count34
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
Denver Nuggets
 
81459
Portland Trail Blazers
 
81320
Golden State Warriors
 
81277
Phoenix Suns
 
81237
Sacramento Kings
 
81125
Other values (29)
1994855
ValueCountFrequency (%) 
Denver Nuggets814593.4%
 
Portland Trail Blazers813203.4%
 
Golden State Warriors812773.4%
 
Phoenix Suns812373.4%
 
Sacramento Kings811253.4%
 
Washington Wizards811123.4%
 
Los Angeles Lakers808003.4%
 
Milwaukee Bucks807863.4%
 
Philadelphia 76ers806823.4%
 
Minnesota Timberwolves806083.4%
 
Other values (24)159086766.3%
 
2021-09-13T22:13:28.304905image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length22
Median length15
Mean length15.93987189
Min length9

PERIOD
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count8
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2.48166743223282
Minimum1
Maximum8
Zeros0
Zeros (%)0.0%
Memory size18.3 MiB
2021-09-13T22:13:28.455731image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1
Q11
median2
Q33
95-th percentile4
Maximum8
Range7
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.137764367
Coefficient of variation (CV)0.4584677027
Kurtosis-1.207269808
Mean2.481667432
Median Absolute Deviation (MAD)1
Skewness0.09535472525
Sum5959161
Variance1.294507754
2021-09-13T22:13:28.568433image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
162502326.0%
 
260087625.0%
 
358882124.5%
 
456973323.7%
 
5144440.6%
 
619340.1%
 
7369< 0.1%
 
873< 0.1%
 
ValueCountFrequency (%) 
162502326.0%
 
260087625.0%
 
358882124.5%
 
456973323.7%
 
5144440.6%
 
ValueCountFrequency (%) 
873< 0.1%
 
7369< 0.1%
 
619340.1%
 
5144440.6%
 
456973323.7%
 

MINUTES_REMAINING
Real number (ℝ≥0)

ZEROS

Distinct count13
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.334222306251726
Minimum0
Maximum12
Zeros244457
Zeros (%)10.2%
Memory size18.3 MiB
2021-09-13T22:13:28.719250image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median5
Q38
95-th percentile11
Maximum12
Range12
Interquartile range (IQR)6

Descriptive statistics

Standard deviation3.461527875
Coefficient of variation (CV)0.6489283116
Kurtosis-1.225869232
Mean5.334222306
Median Absolute Deviation (MAD)3
Skewness0.01445043173
Sum12808924
Variance11.98217523
2021-09-13T22:13:28.818464image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
024445710.2%
 
92028778.4%
 
102014438.4%
 
82009088.4%
 
72005598.4%
 
11999028.3%
 
41990578.3%
 
61989418.3%
 
31984738.3%
 
51963698.2%
 
Other values (3)35828714.9%
 
ValueCountFrequency (%) 
024445710.2%
 
11999028.3%
 
21945358.1%
 
31984738.3%
 
41990578.3%
 
ValueCountFrequency (%) 
1242< 0.1%
 
111637106.8%
 
102014438.4%
 
92028778.4%
 
82009088.4%
 

SECONDS_REMAINING
Real number (ℝ≥0)

ZEROS

Distinct count60
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean28.780207414983636
Minimum0
Maximum59
Zeros66705
Zeros (%)2.8%
Memory size18.3 MiB
2021-09-13T22:13:28.962425image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile2
Q114
median29
Q344
95-th percentile56
Maximum59
Range59
Interquartile range (IQR)30

Descriptive statistics

Standard deviation17.44793333
Coefficient of variation (CV)0.6062476576
Kurtosis-1.195070657
Mean28.78020741
Median Absolute Deviation (MAD)15
Skewness0.008026500944
Sum69109135
Variance304.4303775
2021-09-13T22:13:29.072156image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
0667052.8%
 
1488992.0%
 
2438161.8%
 
3418611.7%
 
43413781.7%
 
45412191.7%
 
41411871.7%
 
4411221.7%
 
44410721.7%
 
42410041.7%
 
Other values (50)195301081.3%
 
ValueCountFrequency (%) 
0667052.8%
 
1488992.0%
 
2438161.8%
 
3418611.7%
 
4411221.7%
 
ValueCountFrequency (%) 
59368921.5%
 
58365801.5%
 
57364621.5%
 
56371311.5%
 
55365191.5%
 

EVENT_TYPE
Categorical

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
Missed Shot
1304339
Made Shot
1096934
ValueCountFrequency (%) 
Missed Shot130433954.3%
 
Made Shot109693445.7%
 
2021-09-13T22:13:29.266918image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length11
Median length11
Mean length10.08637294
Min length9

ACTION_TYPE
Categorical

HIGH CARDINALITY

Distinct count65
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
Jump Shot
1118449
Layup Shot
230297
Driving Layup Shot
 
168181
Pullup Jump shot
 
147516
Step Back Jump shot
 
51518
Other values (60)
685312
ValueCountFrequency (%) 
Jump Shot111844946.6%
 
Layup Shot2302979.6%
 
Driving Layup Shot1681817.0%
 
Pullup Jump shot1475166.1%
 
Step Back Jump shot515182.1%
 
Floating Jump shot427821.8%
 
Hook Shot399871.7%
 
Dunk Shot379261.6%
 
Turnaround Jump Shot379171.6%
 
Fadeaway Jump Shot356351.5%
 
Other values (55)49106520.5%
 
2021-09-13T22:13:29.438566image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length34
Median length9
Mean length12.96793867
Min length7

SHOT_TYPE
Categorical

HIGH CORRELATION

Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
2PT Field Goal
1696456
3PT Field Goal
704817
ValueCountFrequency (%) 
2PT Field Goal169645670.6%
 
3PT Field Goal70481729.4%
 
2021-09-13T22:13:29.584113image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length14
Median length14
Mean length14
Min length14

SHOT_ZONE_BASIC
Categorical

HIGH CORRELATION

Distinct count7
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
Restricted Area
772051
Mid-Range
560425
Above the Break 3
525668
In The Paint (Non-RA)
364243
Left Corner 3
 
90030
Other values (2)
 
88856
ValueCountFrequency (%) 
Restricted Area77205132.2%
 
Mid-Range56042523.3%
 
Above the Break 352566821.9%
 
In The Paint (Non-RA)36424315.2%
 
Left Corner 3900303.7%
 
Right Corner 3836743.5%
 
Backcourt51820.2%
 
2021-09-13T22:13:29.768900image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length21
Median length15
Mean length14.8248504
Min length9

SHOT_ZONE_AREA
Categorical

Distinct count6
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
Center(C)
1323862
Right Side Center(RC)
286018
Left Side Center(LC)
283229
Left Side(L)
 
257697
Right Side(R)
 
244567
ValueCountFrequency (%) 
Center(C)132386255.1%
 
Right Side Center(RC)28601811.9%
 
Left Side Center(LC)28322911.8%
 
Left Side(L)25769710.7%
 
Right Side(R)24456710.2%
 
Back Court(BC)59000.2%
 
2021-09-13T22:13:29.943258image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length21
Median length9
Mean length12.4684078
Min length9

SHOT_ZONE_RANGE
Categorical

HIGH CORRELATION

Distinct count5
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
Less Than 8 ft.
995653
24+ ft.
698654
16-24 ft.
368887
8-16 ft.
332179
Back Court Shot
 
5900
ValueCountFrequency (%) 
Less Than 8 ft.99565341.5%
 
24+ ft.69865429.1%
 
16-24 ft.36888715.4%
 
8-16 ft.33217913.8%
 
Back Court Shot59000.2%
 
2021-09-13T22:13:30.140480image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length15
Median length9
Mean length10.78231755
Min length7

SHOT_DISTANCE
Real number (ℝ≥0)

ZEROS

Distinct count90
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean12.62536871067971
Minimum0
Maximum89
Zeros256926
Zeros (%)10.7%
Memory size18.3 MiB
2021-09-13T22:13:30.326720image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q12
median13
Q323
95-th percentile26
Maximum89
Range89
Interquartile range (IQR)21

Descriptive statistics

Standard deviation10.12164103
Coefficient of variation (CV)0.8016907281
Kurtosis-0.6546963137
Mean12.62536871
Median Absolute Deviation (MAD)11
Skewness0.2548096617
Sum30316957
Variance102.4476172
2021-09-13T22:13:30.436812image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
025692610.7%
 
125487910.6%
 
252069928.6%
 
21778547.4%
 
241686817.0%
 
231058144.4%
 
261046424.4%
 
3823923.4%
 
18699282.9%
 
17677232.8%
 
Other values (80)90544237.7%
 
ValueCountFrequency (%) 
025692610.7%
 
125487910.6%
 
21778547.4%
 
3823923.4%
 
4614622.6%
 
ValueCountFrequency (%) 
892< 0.1%
 
881< 0.1%
 
872< 0.1%
 
8610< 0.1%
 
8512< 0.1%
 

LOC_X
Real number (ℝ)

ZEROS

Distinct count501
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean-0.6563252075045195
Minimum-250
Maximum250
Zeros153237
Zeros (%)6.4%
Memory size18.3 MiB
2021-09-13T22:13:30.589984image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-250
5-th percentile-207
Q1-52
median0
Q350
95-th percentile201
Maximum250
Range500
Interquartile range (IQR)102

Descriptive statistics

Standard deviation109.6600497
Coefficient of variation (CV)-167.0818802
Kurtosis-0.06020020608
Mean-0.6563252075
Median Absolute Deviation (MAD)51
Skewness-0.03041592098
Sum-1576016
Variance12025.3265
2021-09-13T22:13:31.003934image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01532376.4%
 
2348841.5%
 
-2346211.4%
 
1340831.4%
 
9321891.3%
 
6314201.3%
 
4291801.2%
 
-4268291.1%
 
-5248921.0%
 
-7230211.0%
 
Other values (491)197691782.3%
 
ValueCountFrequency (%) 
-250179< 0.1%
 
-2497< 0.1%
 
-248319< 0.1%
 
-24727< 0.1%
 
-246559< 0.1%
 
ValueCountFrequency (%) 
250104< 0.1%
 
2498< 0.1%
 
248192< 0.1%
 
247235< 0.1%
 
246224< 0.1%
 

LOC_Y
Real number (ℝ)

ZEROS

Distinct count894
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean85.07121805808836
Minimum-52
Maximum884
Zeros109140
Zeros (%)4.5%
Memory size18.3 MiB
2021-09-13T22:13:31.145373image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum-52
5-th percentile-3
Q19
median45
Q3160
95-th percentile247
Maximum884
Range936
Interquartile range (IQR)151

Descriptive statistics

Standard deviation90.44396919
Coefficient of variation (CV)1.063155921
Kurtosis0.9365197495
Mean85.07121806
Median Absolute Deviation (MAD)45
Skewness0.9712933698
Sum204279219
Variance8180.111564
2021-09-13T22:13:31.240165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
01091404.5%
 
1682482.8%
 
11575712.4%
 
7551072.3%
 
6445041.9%
 
3443201.8%
 
4407391.7%
 
9337461.4%
 
15327521.4%
 
-6319331.3%
 
Other values (884)188321378.4%
 
ValueCountFrequency (%) 
-521< 0.1%
 
-5124< 0.1%
 
-4919< 0.1%
 
-481< 0.1%
 
-4719< 0.1%
 
ValueCountFrequency (%) 
8841< 0.1%
 
8771< 0.1%
 
8671< 0.1%
 
8651< 0.1%
 
8631< 0.1%
 

SHOT_ATTEMPTED_FLAG
Boolean

CONSTANT
REJECTED

Distinct count1
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
1
2401273
ValueCountFrequency (%) 
12401273100.0%
 
Distinct count2
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
0
1304339
1
1096934
ValueCountFrequency (%) 
0130433954.3%
 
1109693445.7%
 

GAME_DATE
Real number (ℝ≥0)

HIGH CORRELATION

Distinct count1885
Unique (%)0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean20152558.751388952
Minimum20091027
Maximum20210516
Zeros0
Zeros (%)0.0%
Memory size18.3 MiB
2021-09-13T22:13:31.378273image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Quantile statistics

Minimum20091027
5-th percentile20100205
Q120121201
median20151117
Q320180411
95-th percentile20210215
Maximum20210516
Range119489
Interquartile range (IQR)59210

Descriptive statistics

Standard deviation34406.02302
Coefficient of variation (CV)0.00170727814
Kurtosis-1.083732596
Mean20152558.75
Median Absolute Deviation (MAD)29907
Skewness-0.01606109147
Sum4.839179521e+13
Variance1183774420
2021-09-13T22:13:31.503967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%) 
2021051627020.1%
 
2014041625740.1%
 
2016112525660.1%
 
2019040725630.1%
 
2013041725350.1%
 
2020012025320.1%
 
2018112325090.1%
 
2011041324920.1%
 
2019112724870.1%
 
2019122824840.1%
 
Other values (1875)237582998.9%
 
ValueCountFrequency (%) 
20091027632< 0.1%
 
2009102819360.1%
 
20091029304< 0.1%
 
2009103021080.1%
 
200910311081< 0.1%
 
ValueCountFrequency (%) 
2021051627020.1%
 
202105151104< 0.1%
 
2021051414150.1%
 
2021051315910.1%
 
202105121083< 0.1%
 

HTM
Categorical

Distinct count32
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
GSW
 
81644
PHX
 
81616
HOU
 
81536
LAL
 
81386
DEN
 
81277
Other values (27)
1993814
ValueCountFrequency (%) 
GSW816443.4%
 
PHX816163.4%
 
HOU815363.4%
 
LAL813863.4%
 
DEN812773.4%
 
POR808773.4%
 
OKC808053.4%
 
SAC807423.4%
 
WAS804923.4%
 
IND803983.3%
 
Other values (22)159050066.2%
 
2021-09-13T22:13:31.689568image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

VTM
Categorical

Distinct count32
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
LAL
 
81594
GSW
 
81500
DEN
 
81209
OKC
 
81119
SAC
 
81093
Other values (27)
1994758
ValueCountFrequency (%) 
LAL815943.4%
 
GSW815003.4%
 
DEN812093.4%
 
OKC811193.4%
 
SAC810933.4%
 
POR810613.4%
 
PHX809603.4%
 
MIL808823.4%
 
MIN808683.4%
 
HOU808453.4%
 
Other values (22)159014266.2%
 
2021-09-13T22:13:31.840260image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length3
Median length3
Mean length3
Min length3

SEASON_ID
Categorical

Distinct count12
Unique (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size18.3 MiB
2018-19
 
219458
2017-18
 
211707
2016-17
 
209929
2015-16
 
207893
2014-15
 
205550
Other values (7)
1346736
ValueCountFrequency (%) 
2018-192194589.1%
 
2017-182117078.8%
 
2016-172099298.7%
 
2015-162078938.7%
 
2014-152055508.6%
 
2013-142041268.5%
 
2012-132015798.4%
 
2009-102009668.4%
 
2010-111997618.3%
 
2020-211909838.0%
 
Other values (2)34932114.5%
 
2021-09-13T22:13:31.987168image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Length

Max length7
Median length7
Mean length7
Min length7

Interactions

2021-09-13T22:10:40.488210image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:41.518277image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:42.538391image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:43.470453image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:44.460251image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:45.470518image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:46.510085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:47.486423image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:48.774557image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:49.876771image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:50.829311image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:51.867033image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:52.840931image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:53.864237image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:54.911552image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:56.042525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:57.226106image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:58.236612image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:10:59.228281image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:00.269793image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:01.223434image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:02.285147image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:03.293274image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:04.243009image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:05.201057image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:06.208426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:07.295025image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:08.249253image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:09.247616image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:10.196658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:11.172940image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:12.195152image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:13.200058image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:14.192935image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:15.155648image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:16.124645image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:17.125383image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:18.140187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:19.161170image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:20.156987image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:21.261553image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:22.173350image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:23.193371image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:24.201832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:25.153874image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:26.155592image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:27.178878image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:28.214426image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:29.306657image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:30.450722image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:31.609100image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:32.691298image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:33.836583image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:34.746245image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:35.684441image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:36.633960image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:37.659212image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:38.709626image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:39.714715image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:40.694897image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:41.659085image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:42.586381image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:43.615606image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:44.527278image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:45.452604image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:46.318658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:47.281658image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:48.245225image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:49.212239image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:50.192631image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:51.164559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:52.137182image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:53.085787image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:54.103718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:55.066639image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:55.976053image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:56.927432image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:57.893477image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:59.018490image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:11:59.979562image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:00.951559image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:01.971292image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:02.917785image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:03.924158image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:05.046324image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:06.069693image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:07.132992image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:08.163395image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:09.230816image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:10.167875image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:11.106672image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:12.058187image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:13.016928image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:14.043823image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:15.073549image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:15.983132image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:16.910232image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:17.847718image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:18.853599image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:19.848234image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:20.853525image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:21.756652image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:22.731071image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:23.651505image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:24.629982image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:25.621865image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:26.536375image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:27.451461image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:28.472702image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:29.481841image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:30.555641image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:31.533103image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:32.538438image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:33.510235image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:34.491366image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:35.538618image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:36.508832image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:37.501801image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:38.459178image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:39.569979image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:40.634164image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:41.690485image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:42.786034image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:43.858116image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:44.908772image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:45.902262image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:46.934863image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:47.880967image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:48.823985image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:49.875052image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:50.885439image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:51.849795image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:52.873957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:53.880838image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:54.866857image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:55.921165image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:56.906351image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:57.849775image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:58.809624image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:12:59.855628image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:13:00.912077image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:13:01.929957image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:13:02.918797image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:13:03.931224image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Correlations

2021-09-13T22:13:32.127451image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2021-09-13T22:13:32.353259image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2021-09-13T22:13:32.577496image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2021-09-13T22:13:32.837792image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.
2021-09-13T22:13:33.096303image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.

Missing values

2021-09-13T22:13:07.988796image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:13:12.533847image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/
2021-09-13T22:13:23.297354image/svg+xmlMatplotlib v3.3.2, https://matplotlib.org/

Sample

First rows

df_indexGRID_TYPEGAME_IDGAME_EVENT_IDPLAYER_IDPLAYER_NAMETEAM_IDTEAM_NAMEPERIODMINUTES_REMAININGSECONDS_REMAININGEVENT_TYPEACTION_TYPESHOT_TYPESHOT_ZONE_BASICSHOT_ZONE_AREASHOT_ZONE_RANGESHOT_DISTANCELOC_XLOC_YSHOT_ATTEMPTED_FLAGSHOT_MADE_FLAGGAME_DATEHTMVTMSEASON_ID
00Shot Chart Detail2200001227203932Aaron Gordon1610612753Orlando Magic1956Missed ShotJump Shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.24-1461991020201223ORLMIA2020-21
11Shot Chart Detail2200001240203932Aaron Gordon1610612753Orlando Magic1855Made ShotRunning Dunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0-451120201223ORLMIA2020-21
22Shot Chart Detail2200001260203932Aaron Gordon1610612753Orlando Magic1710Missed ShotStep Back Jump shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.25-1541981020201223ORLMIA2020-21
33Shot Chart Detail2200001264203932Aaron Gordon1610612753Orlando Magic1634Made ShotDunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0-4-41120201223ORLMIA2020-21
44Shot Chart Detail2200001275203932Aaron Gordon1610612753Orlando Magic1536Made ShotTip Layup Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0001120201223ORLMIA2020-21
55Shot Chart Detail22000012183203932Aaron Gordon1610612753Orlando Magic2950Missed ShotStep Back Jump shot3PT Field GoalAbove the Break 3Left Side Center(LC)24+ ft.25-1871711020201223ORLMIA2020-21
66Shot Chart Detail22000012249203932Aaron Gordon1610612753Orlando Magic2551Made ShotLayup Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.2-4271120201223ORLMIA2020-21
77Shot Chart Detail22000012515203932Aaron Gordon1610612753Orlando Magic41134Made ShotJump Shot3PT Field GoalRight Corner 3Right Side(R)24+ ft.23230411120201223ORLMIA2020-21
88Shot Chart Detail22000012539203932Aaron Gordon1610612753Orlando Magic4941Made ShotJump Shot2PT Field GoalMid-RangeRight Side(R)16-24 ft.19167941120201223ORLMIA2020-21
99Shot Chart Detail22000012557203932Aaron Gordon1610612753Orlando Magic4833Made ShotRunning Alley Oop Dunk Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.26261120201223ORLMIA2020-21

Last rows

df_indexGRID_TYPEGAME_IDGAME_EVENT_IDPLAYER_IDPLAYER_NAMETEAM_IDTEAM_NAMEPERIODMINUTES_REMAININGSECONDS_REMAININGEVENT_TYPEACTION_TYPESHOT_TYPESHOT_ZONE_BASICSHOT_ZONE_AREASHOT_ZONE_RANGESHOT_DISTANCELOC_XLOC_YSHOT_ATTEMPTED_FLAGSHOT_MADE_FLAGGAME_DATEHTMVTMSEASON_ID
2401263200956Shot Chart Detail20901195413980Zydrunas Ilgauskas1610612739Cleveland Cavaliers4813Missed ShotJump Shot2PT Field GoalMid-RangeLeft Side(L)8-16 ft.14-144141020100411CLEORL2009-10
2401264200957Shot Chart Detail20901195438980Zydrunas Ilgauskas1610612739Cleveland Cavaliers4545Missed ShotLayup Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.0171020100411CLEORL2009-10
2401265200958Shot Chart Detail20901195439980Zydrunas Ilgauskas1610612739Cleveland Cavaliers4544Made ShotTip Shot2PT Field GoalRestricted AreaCenter(C)Less Than 8 ft.19-51120100411CLEORL2009-10
2401266200959Shot Chart Detail20901195442980Zydrunas Ilgauskas1610612739Cleveland Cavaliers4518Missed ShotJump Shot2PT Field GoalMid-RangeLeft Side Center(LC)16-24 ft.17-1121341020100411CLEORL2009-10
2401267200960Shot Chart Detail2090121775980Zydrunas Ilgauskas1610612739Cleveland Cavaliers1240Missed ShotJump Shot2PT Field GoalMid-RangeLeft Side Center(LC)16-24 ft.17-1301101020100414ATLCLE2009-10
2401268200961Shot Chart Detail20901217112980Zydrunas Ilgauskas1610612739Cleveland Cavaliers1019Made ShotTurnaround Jump Shot2PT Field GoalIn The Paint (Non-RA)Center(C)8-16 ft.10-71081120100414ATLCLE2009-10
2401269200962Shot Chart Detail20901217160980Zydrunas Ilgauskas1610612739Cleveland Cavaliers274Missed ShotTurnaround Jump Shot2PT Field GoalMid-RangeLeft Side(L)8-16 ft.14-144311020100414ATLCLE2009-10
2401270200963Shot Chart Detail20901217196980Zydrunas Ilgauskas1610612739Cleveland Cavaliers2454Missed ShotJump Shot2PT Field GoalMid-RangeCenter(C)16-24 ft.17471701020100414ATLCLE2009-10
2401271200964Shot Chart Detail20901217396980Zydrunas Ilgauskas1610612739Cleveland Cavaliers41139Missed ShotHook Shot2PT Field GoalIn The Paint (Non-RA)Center(C)8-16 ft.1071051020100414ATLCLE2009-10
2401272200965Shot Chart Detail20901217449980Zydrunas Ilgauskas1610612739Cleveland Cavaliers4724Made ShotJump Shot2PT Field GoalMid-RangeRight Side Center(RC)16-24 ft.171001421120100414ATLCLE2009-10